iotoolz 0.1.0

Consistent io iterface to read and write from/to both local and different remote resources (e.g. http, s3, minio)

iotoolz

iotoolz is an improvement over e2fyi-utils and is inspired partly by toolz. iotoolz is a lib to help provide a consistent dev-x for interacting with any IO resources. It provides an abstract class iotoolz.AbcStream which mimics python's native open very closely (with some additional parameters and methods such as save).

API documentation can be found at https://iotoolz.readthedocs.io/en/latest/.

Change logs are available in CHANGELOG.md.

• Python 3.6.1 and above
• Licensed under Apache-2.0.

Supported streams

Current the following streams are supported:

• iotoolz.FileStream: wrapper over built-in open function (file://)
• iotoolz.TempStream: in-memory stream that will rollover to disk (tmp://, temp://)
• iotoolz.HttpStream: http or https stream implemented with requests (http://, https://)
• iotoolz.extensions.S3Stream: s3 stream implemented with boto3 (s3://, s3a://, s3n://)
• iotoolz.extensions.MinioStream: s3 stream implemented with minio (minio://)

Installation

# install the default packages only (most lite-weight)
pip install iotoolz

# install dependencies for specific extension
pip install iotoolz[boto3]

# install all the extras
pip install iotoolz[all]

Available extras:

• all: All the optional dependencies
• boto3: boto3 for iotoolz.extensions.S3Stream
• minio: minio for iotoolz.extensions.MinioStream
• chardet: install LGPL chardet for guessing encoding. Otherwise charset_normalizer will be used.

Quickstart

The helper object iotoolz.streams.stream_factory is a default singleton of iotoolz.streams.Streams provided to support most of the common use cases.

iotoolz.streams.open_stream (alias iotoolz.streams.Stream) and is a util method provided by the singleton helper to create a stream object. This method accepts the same arguments as python's open method with the following additional parameters:

• data: optional str or bytes that will be passed into the stream
• fileobj: optional file-like object which will be copied into the stream
• content_type: optional mime type information to describe the stream (e.g. application/json)
• inmem_size: determines how much memory to allocate to the stream before rolling over to local file system. Defaults to no limits (may result in MemoryError).
• schema_kwargs: optional mapping of schemas to their default kwargs.

Basic Setup

from iotoolz.streams import (
    set_schema_kwargs,
    set_buffer_rollover_size,
)

# set params to pass to the Stream obj handling https
# i.e. HttpStream (implemented with requests)
set_schema_kwargs(
    "https",
    verify=False,  # do not verify ssl cert
    use_post=True  # use POST instead of PUT when writing to https
)

# use a custom client for S3Stream (via boto3)
set_schema_kwargs(
    "s3",
    client=boto3.client(
        "s3",
        aws_access_key_id=ACCESS_KEY,
        aws_secret_access_key=SECRET_KEY,
        aws_session_token=SESSION_TOKEN,
    )
)

# use a custom credentials for MinioStream
set_schema_kwargs(
    "minio",
    access_key=ACCESS_KEY,
    secret_key=SECRET_KEY,
    secure=True,
)

# buffer will rollover to disk if the data is more than 100 MB
# (default is everything is in-memory - may result in memory error)
set_buffer_rollover_size(10**8)

Opening streams

You can open any stream just like python's built-in open method.

import pandas

from iotoolz import open_stream

# print line by line some data in from a https endpoint
# and do not verify the ssl cert of the https endpoint
with open_stream(
    "https://foo/bar/data.txt",
    mode="r",
    schema_kwargs={"https": {"verify": False}}
) as stream:
    for line in stream:
        print(line)

# POST some binary content to a http endpoint (default is PUT)
with open_stream("https://foo.bar/api/data", "wb", use_post=True) as stream:
    stream.write(b"hello world")

# Copying a local file to s3
with open_stream("path/to/data.csv", "r") as csv_source,
     open_stream("s3://bucket/foobar.txt?StorageClass=STANDARD", "w") as s3_sink:
    # pipe content in csv_source to tmpsink
    csv_source.pipe(s3_sink)

# load to pandas dataframe from s3 fileobj
with open_stream("s3://bucket/foobar.csv", "r") as csv:
    df = pd.read_csv(csv)

TempStream

TempStream is a stream can functions like a virtual file system in memory.

import gc

from iotoolz import Stream, exists, glob, iter_dir

# this stream can be garbage collected
Stream("tmp://foo/bar/data.txt", data="foobar")

# True if not gc yet, False if already gc
exists("tmp://foo/bar/data.txt")

# force gc
gc.collect()
# will not exist
exists("tmp://foo/bar/data.txt")

# create temp stream with strong ref (hence will not be gc)
s1 = Stream("tmp://foo/bar/data.txt", data="foobar")
s2 = Stream("tmp://foo/example.txt", data="...")

# returns s1 and s2
iter_dir("tmp://foo/")

# returns s1 only
glob("tmp://foo/bar/*.txt")

Stream-like operations

Stream is an alias of open_stream, both methods return a concrete AbcStream object. You can treat the object as both a "file-like" and "stream-like" object - i.e. you can read, write, seek, flush, close the object.

NOTE

By default, the underlying buffer is in-memory. You can enable rollover to disk by passing the inmem_size arg to the method, or update the default inmem_size value with the iotoolz.streams.set_buffer_rollover_size method.

from iotoolz import open_stream, Stream, set_buffer_rollover_size

# `Stream` is an alias of `open_stream`
assert open_stream == Stream

# rollover to disk if data is over 100 MB
set_buffer_rollover_size(10**8)

# you can overwrite the default kwargs here also
stream = Stream(
    "path/to/data.txt",
    mode="rw",  # you can both read and write to a stream
)
# stream is lazily evaluated, nothing will be buffered until you call some methods
# that requires the data
data = stream.read()
# will attempt to provide encoding and content_type if not provided when opening the stream
print(stream.encoding)
print(stream.content_type)
# stream has the same interface as an IO object - i.e. u can seek, flush, close, etc
stream.seek(5)  # go to offset 5 from start of buffer
stream.write("replace with this text")
stream.seek(0, whence=2)  # go to end of buffer
stream.write("additional text after original eof")  # continue writing to the end of the buffer
stream.save()  # flush save the entire buffer to the same dst location
stream.close() # close the stream

Path-like operations

exists, mkdir, iter_dir and glob are path-like methods that are available to the stream object. These methods mimics their equivalent in pathlib.Path when appropriate.

method	supported streams	desc
stats	All Streams	return the StreamInfo for an existing resource
unlink, delete, remove	All Streams	Delete and remove the stream (except for TempStream where the buffer is cleared instead)
exists	All Streams	check if a stream points to an existing resource.
mkdir	FileStream	create a directory.
rmdir	FileStream, TempStream, and S3Stream,	remove recursively everything in the directory.
iter_dir	FileStream, TempStream, and S3Stream	iterate thru the streams in the directory.
glob	FileStream, TempStream, and S3Stream	iterate thru the streams in the directory that match a pattern.

import itertools

from iotoolz import Stream, mkdir, iter_dir, glob, exists

# similar to 'mkdir -p'
mkdir("path/to/folder", parents=True, exist_ok=True)
Stream("path/to/folder").mkdir(parents=True, exist_ok=True)

# list object in an s3 bucket
iter_dir("s3://bucket/prefix/")
for stream in Stream("s3://bucket/prefix/").iter_dir():
    print(stream.uri)

# find s3 objects with a specific pattern
glob("s3://bucket/prefix/*txt")
for stream in Stream("s3://bucket/prefix/").glob("*.txt"):
    print(stream.uri)

# exists
exists("s3://bucket/prefix/foo.txt")

# stats
info = stats("s3://bucket/prefix/foo.txt")
print(info.name)
print(info.content_type)
print(info.encoding)
print(info.last_modified)
print(info.etag)
print(info.extras)

# delete resource
unlink("s3://bucket/prefix/foo.txt")

# rm all key with prefix
rmdir("s3://bucket/prefix/")

Piping streams

pipe is method to push data to a sink (similar to NodeJS stream except it has no watermark or buffering).

from  iotoolz.streams import open_stream

local_file = open_stream(
    "path/to/google.html", content_type="text/html", mode="w"
)
temp_file = open_stream(
    "tmp://google.html", content_type="text/html", mode="wb"
)

# when source is closed, all sinks will be closed also
with open_stream("https://google.com") as source:
    # writes to a temp file then to a local file in sequence
    source.pipe(temp_file).pipe(local_file)


local_file2 = open_stream(
    "path/to/google1.html", content_type="text/html", mode="w"
)
local_file3 = open_stream(
    "path/to/google2.html", content_type="text/html", mode="w"
)

# when source is closed, all sinks will be closed also
with open_stream("tmp://foo_src", mode="w") as source:
    # writes in a fan shape manner
    source.pipe(local_file2)
    source.pipe(local_file3)

    source.write("hello world")

TODO support transform streams so that pipe can be more useful

License